Estimating Brain Activation Patterns from EEG Data

Brain injury is a common cause of death and critical illness among children and young adults. It is possible to prevent secondary damage to the brain if we know the situation in time to respond. Traditionally, monitoring the patient’s health is done manually by observations, or using expensive systems such as MRI which are few in number and take a long time to process. We propose using EEG signals as a cheap and simple substitute, allowing for quick deployment at the expense of accurate data.
EEG signals are noisy and difficult to analyse. Therefore, we examine different ways to map the data – Diffusion maps, PCA, etc., over a Riemannian metric in the manifold of covariance matrices [1]. Searching for a space in which the partition between sick and healthy subjects is optimal.
In addition, we study the ability to differentiate between different stimulations for one subject.
We show that the Riemannian metric is indeed a good way to describe the data, while there is the advantage at prediction using diffusion maps over PCA (96.5% compared to 79.6%), whereas PCA has the advantage of demanding less time and memory for its computations, and also the ease with which one can add more data.
On the whole, we find that using EEG signals in order to differentiate between subjects with brain injuries and without them is indeed feasible.